Variational relational point completion network for robust 3D classification
Real-scanned point clouds are often incomplete due to viewpoint, occlusion, and noise, which hampers 3D geometric modeling and perception. Existing point cloud completion methods tend to generate global shape skeletons and hence lack fine local details. Furthermore, they mostly learn a deterministic...
Saved in:
Main Authors: | Pan, Liang, Chen, Xinyi, Cai, Zhongang, Zhang, Junzhe, Zhao, Haiyu, Yi, Shuai, Liu, Ziwei |
---|---|
Other Authors: | School of Computer Science and Engineering |
Format: | Article |
Language: | English |
Published: |
2023
|
Subjects: | |
Online Access: | https://hdl.handle.net/10356/172185 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Institution: | Nanyang Technological University |
Language: | English |
Similar Items
-
3D object recovery and stylization with limited supervision
by: Zhang, Junzhe
Published: (2024) -
Robust partial-to-partial point cloud registration in a full range
by: Pan, Liang, et al.
Published: (2024) -
BEACon : a boundary embedded attentional convolution network for point cloud instance segmentation
by: Liu, Tianrui, et al.
Published: (2021) -
POINT CLOUD RECOGNITION WITH DEEP LEARNING
by: LI JIAXIN
Published: (2018) -
3D point cloud attribute compression using geometry-guided sparse representation
by: Gu, Shuai, et al.
Published: (2021)